POS0975 VALIDATION OF INTERFERON STIMULATED GENE SCORES, PREVIOUSLY DEVELOPED IN PERIPHERAL BLOOD MONONUCLEAR CELLS, IN WHOLE BLOOD SAMPLES
Authors: M. Wilson, Z. Wigston, A. Burska, A. Alase, D. Plant, E. Hensor, E. Vital
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> A recent EULAR Task force identified critical variability in interferon (IFN) assay methods and defined a research agenda to improve and harmonise assays. IFN scores A and B, developed using IFN stimulated genes (ISG) measured in peripheral blood mononuclear cells (PBMC) differentiate between clinical subsets of patients with autoimmune disease [1]. Whole blood (WB) samples are more commonly used in clinical practice and trials. Regardless of sample type, real time polymerase chain reaction (RT-PCR) experiments require a suitable reference gene (RG) which is uniformly expressed across different experimental conditions in order to normalise target gene expression quantities [2]. <h3>Objectives</h3> (i) To determine whether sample type affects the previously defined IFN scores; (ii) if so, to develop revised IFN scores in WB; (iii) to identify the best RG for future RT-PCR experiments. <h3>Methods</h3> Samples were collected from participants enrolled in 5 studies; DEFINITION, CONVAS, USEFUL, MASTERPLANs and BRAGGSS. PBMCs were separated from blood using leucosep tubes and WB was collected using Tempus™ Blood RNA Tubes. Undetected delta Ct values were singly imputed. IFN scores A and B were calculated in paired WB and PBMC samples and agreement between the sample types was assessed via the Bland-Altman (BA) method. New IFN scores for WB were derived via exploratory factor analysis (EFA) of 31 ISG as per the original work [1]. Separation of clinical subgroups was visualized using violin plots and effect sizes calculated using Hedges' g. RG identification from 16 candidates used online software. <h3>Results</h3> We used samples from 342 participants with systemic lupus erythematosus (SLE), 6 ANA positive at risk progressors, 7 at risk non-progressors, 30 with rheumatoid arthritis (RA) and 10 healthy controls (HC). Subsets were used for sample comparison (n=45) and RG work (n=22). Comparing score A in PBMC versus WB matched samples resulted in bias of -1.44, limits of agreement (LOA 0.08, -2.96), the spread of data on BA plot showed a systematic difference. The same comparison in score B gave bias of -1.23 (LOA 0.26, 2.73), data on BA plot suggested differences were not systematic. EFA in WB produced a two-factor solution which explained 97% of the variation. The first (score C) contained 3 ISG and the second (score D) contained 4 (Table 1), compared with score A (12 ISG) and score B (14 ISG) [1]. Both scores showed potential to differentiate between HC and those with SLE or RA, but neither separated SLE from RA as the original PBMC scores A and B had done (Figure 1). YWHAZ, PGK1 and GUSB were the most stable RG. Others such as ACTB performed poorly as RG. <h3>Conclusion</h3> PBMC and WB may be used interchangeably to calculate IFN score A, but not score B. This suggests an influence of neutrophils, including the demethylation of neutrophil ISG cytosine-guanine sites, on Score B genes [3]. In WB we derived a further two scores which differentiated SLE or RA from HC, although neither score distinguished between SLE and RA. Further work is needed to assess the performance of scores A-D in WB samples. The Minimum Information for Publication of Quantitative Real-Time PCR Experiments recommend using ≥3 RG to avoid bias[4]; we found that YWHAZ, PGK1 and GUSB were the most stable genes across a range of experimental conditions. <h3>References</h3> [1]El-Sherbiny, Y.M. et al., Sci Rep, 2018. 8(1): p. 5793. [2]Schmittgen, T.D. and K.J. Livak, Nat Protoc, 2008. 3(6): p. 1101-8. [3]Coit, P., et al., J Autoimmun, 2015. 58: p. 59-66. [4]Bustin, S.A., et al., BMC Molecular Biology, 2010. 11(1): p. 74. <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> None Declared.